Medical image segmentation, i.e. the extraction of geometric models of anatomical structures in the human body from medical images (Magnetic Resonance, Ultrasound, Computed Tomography, Fluoroscopy is a vital part in many clinical workflows such as diagnostics, procedure planning or imagebased interventional support/guidance.
Segmentation can be tedious and time-consuming, especially if done manually, thus there is a need for advanced tools to automate or interactively support this task to make it more efficient, robust, and reproducible.
However, in many cases the development of such tools is non-trivial as it requires in-depth knowledge of the structure of interest (organ, anatomy, . . . ) to define a set of features and rules that can perform the segmentation task effectively for various structure shapes (pathologies, . . . ) and for varying image quality.
In fact, most segmentation algorithms are tailored to one specific combination of anatomical structure of interest and imaging modality (or acquisition protocol). Therefore, the adaption of an algorithm to new environments, for example regarding the imaging setup and/or the clinical task, or even different image quality can become a challenging and expensive undertaking.
Hence, generically applicable algorithms and/or image features that require less domain-specific knowledge would be desirable. Moreover, an important aspect in developing such tools is the intention of the end-user.
For many segmentation tasks there exists more than one valid segmentation and the correct (intended by user) segmentation may vary based on the use-case or a specific user's preference. For instance, for a segmentation of the left endocardial border of the heart (FIG. 11), one user may expect a precise delineation of the blood-pool from all other structures, while another user may prefer a “smoother” representation that should include the papillary muscles and trabeculae.
Many segmentation algorithms are designed to follow one single intention (e.g. one of the scenarios described above) and once shipped to the user, they cannot be adapted to changing requirements or user preferences. In many clinical scenarios the segmentation task is still performed manually. An expert operator (clinician or technician) has to precisely annotate the structure of interest 2D or 3D medical images using standard image visualization and annotation software. Therefore, the results may not be reproducible and the quality may vary significantly based on the experience of the operator. Moreover, manual segmentation is usually a tedious and time-consuming process.
To overcome some of the problems with manual segmentation, a multitude of automatic or semi-automatic segmentation algorithms have been proposed [1]. Many of them try to solve the segmentation problem by analyzing image intensities, by relying on shape constraints, or by a combination of both. More often than not they are heavily fine-tuned and targeted towards the segmentation of a single, pre-defined structure in the body, and they may work only for a specific imaging modality or acquisition protocol.
Therefore, such algorithms may fail in cases where, for instance, the shape of the structure to be segmented is highly pathological (unseen during development phase) or if the image appearance (distribution of intensities, etc.) differs from typical images. Most automatic algorithms are designed to perform segmentation following strict requirements, usually defined by a clinician or a group of clinicians, i.e. according to a specific user intention. However, as mentioned above, the intentions of different users or for different use-cases can be different and even change over time. Even after the development of the segmentation algorithm has been finalized and the software shipped to the end-user.
There exist few approaches that try to modify some components of an algorithm's behavior based on user inputs [2] that are applicable for basic structures with various simplifying assumptions. Typically, these approaches do not provide general mechanisms to adapt an algorithm to new users or to automatically learn from user interaction (no continuous improvement). In many cases, to modify the automatic segmentation result, tedious manual editing of the same kind is needed every time the outcome does not satisfy the user's requirements. Algorithms of this kind do not adjust themselves.